Recognition and monitoring technology for substations based on regional fully convolutional networks

ZHANG Yaping; WANG Chuyuan; CHENG Hongbo

doi:10.7688/j.issn.1000-1646.2025.04.05

Abstract：[Objective]Substations,as the core hubs of power transmission and distribution,play a crucial role in ensuring the safe and stable operation of power systems,which is essential for efficient and reliable power supply.However,traditional substation monitoring methods face challenges such as limited automatic monitoring capabilities and inadequate target detection accuracy,making it difficult to meet the increasing safety demands of modern power systems.This study aimed to develop substation target recognition and safety monitoring technology based on the regional fully convolutional network(R-FCN)to overcome the shortcomings of traditional monitoring methods,significantly enhance substation safety assurance,and establish a solid foundation for the stable operation of the power system.[Methods]This method combined the unique advantages of region extraction and fully convolutional networks to construct an efficient and intelligent monitoring system.High-definition video surveillance cameras were deployed in the data collection phase to continuously capture real-time image data of the substation from multiple angles,providing massive and precise raw data for subsequent in-depth analysis.The advanced R-FCN model was applied for object detection based on the collected images.Due to its fully convolutional nature,R-FCN could effectively maintain high-resolution feature maps when processing images of different sizes,avoiding the information loss commonly encountered during downsampling with traditional methods,thus significantly improving object detection accuracy.A specially designed area extraction module,resembling an intelligent navigation system,accurately located key facilities such as transformers,switchgear,and insulators within the complex substation environment,ensuring real-time and precise monitoring of equipment status.Moreover,abnormal behaviors,such as unauthorized personnel entering hazardous areas or equipment suddenly emitting smoke or catching fire,were detected promptly,allowing for valuable time to be saved for emergency responses.[Results]Extensive simulation experiments and practical testing in real substation monitoring scenarios demonstrate the system's excellent performance.In comparison with traditional object detection methods,the system significantly improves detection accuracy,enhances monitoring reliability,and reduces unnecessary manpower and resource expenditure.[Conclusion]The R-FCN-based substation target recognition and safety monitoring technology combines efficient real-time processing and precise target positioning capabilities.When handling massive monitoring data,it can quickly and accurately identify various targets and abnormal situations,providing robust technical support for the safe and stable operation of the power system.This technology has profound implications for enhancing substation monitoring levels and ensuring the reliable power supply of the power system.